Due to advances in computing technology, businesses today are able to operate more efficiently when compared to substantially similar businesses only a few years ago. For example, internal networking enables employees of a company to communicate instantaneously by email, quickly transfer data files to disparate employees, manipulate data files, share data relevant to a project to reduce duplications in work product, etc. Furthermore, advancements in technology have enabled factory applications to become partially or completely automated. For instance, operations that once required workers to put themselves proximate to heavy machinery and other various hazardous conditions can now be completed at a safe distance therefrom.
Further, imperfections associated with human action have been minimized through employment of highly precise machines. Many of these factory devices supply data related to manufacturing to databases or web services referencing databases that are accessible by system/process/project managers on a factory floor. For instance, sensors and associated software can detect a number of instances that a particular machine has completed an operation given a defined amount of time. Further, data from sensors can be delivered to a processing unit related to system alarms. Thus, a factory automation system can review collected data and automatically and/or semi-automatically schedule maintenance of a device, replacement of a device, and other various procedures that relate to automating a process.
As can be discerned from the above, a substantial amount of data can be generated in an industrial automation environment with respect to controlling and maintaining an industrial process. Additionally, with tagging devices, such as barcodes and Radio Frequency Identifier tags (RFID tags) becoming more affordable and more prevalent, massive amounts of data relating to items being manufactured can be generated. For example, through employment of RFID tags, location, identification, and other information can be uniquely associated with each item. Thus, if desired, data can be collected and analyzed regarding each item that is being manufactured. Moreover, metadata can be associated with each item as an item is being manufactured. For instance, it may be desirable to know a time that a certain product was in a particular location. Similarly, name of an operator associated with the product can be associated with the product at particular times. Thus, it is apparent that an extremely granular amount of data can be associated with each item being manufactured.
Typically, however, such granularity in tracking items during manufacturing is not desired. In a specific example, thousands of items can be created in a manufacturing facility in a single day. Accordingly, creating and retaining data for each individual item would generally be counterproductive, as it would be extremely difficult to extract meaningful information from such a vast amount of data. Additionally, expense would be incurred in storing the data, since over a short period of time enormous amounts of data can be generated. Therefore, often it is desirable to group items into lots of items and track such lots during manufacturing of items within the lots. A size of the lots (e.g., a number of items within the lots), for example, can be based upon a work order for a particular client. Therefore, it is desirable to track such lot for billing and inventory purposes with respect to the client without commingling tracking data with data relating to other lots.
Most items require several processes to be undertaken in sequence to enable manufacture of the items. For example, manufacturing a bag of potato chips requires steps of receiving potatoes, skinning the potatoes, slicing the potatoes, frying the resulting chips, removing the chips from the fryer and allowing the chips to dry, separating the chips into portions of a desired size, and packaging the chips. Therefore, a single lot of potato chips may be spread over numerous processes. Conventionally, there is no mechanism for tracking lots of items across several processes. Rather, a lot of a particular size is released, and an operator manually splits the lot into individual lots for control purposes (e.g., to control individual processes). Each of these individual lots must then be tracked and identified by the operator as they pass through different processes. Once the lot is complete (e.g., each item within the lot has passed through relevant processes), the operator must manually recombine data associated with each of the individually created sublots.